5 Database Hygiene Best Practices You can Apply Today

Before Database Hygiene Best Practices, Let’s Go Over The Basics

According to Mercury, creative digital experts, 45% of marketers don’t validate their data for quality and accuracy, and 62% use incomplete or invalid prospect data. These numbers may give you an idea of how “dirty data” could stand in your way, driving a wedge between your business goals and the people you need to reach to grow. Implementing database hygiene best practices is an effective solution to this challenge.

But, what exactly is database hygiene, anyway? Data cleaning, data hygiene, or database enrichment are all terms referring to maintaining big data sets free of duplicate data, inaccuracies, and inconsistent information.  

Keeping data clean may seem a daunting task, especially if you have a considerable customer base. However, once you learn some best practices tips, you’ll see the results are well worth your efforts.

Marketing and sales teams often realize there are plenty of issues directly affecting their data’s quality. Is this your case? If so, it is entirely normal. A study from the Harvard Business Review discovered that only 3% of the data quality scores in its study were rated as “acceptable.” 

Forbes says low-quality data is the catalyst for poor business outcomes, resulting in incomplete customer or prospect data, wasted marketing and communications efforts, increased spending, and poor decision-making. 

However, when you take control of the customer’s journey and data and provide a personalized marketing experience across channels, you increase sales efficiency and scale your business to the next level.

Related: Top 5 Marketing Analytics Metrics for the Revenue-Driven Team

Establish a Goal for your Database Cleanup

After learning what database hygiene is, how confident are you in the overall health and quality of the data in your database? Are you maybe recognizing your data is not as accurate as you would like?

Your answers may motivate you to take immediate action, but before you do, you need a data cleanup reality check. 

As Dr. Stephen Covey said in his bestseller The 7 Habits of Highly Effective People, you must start with the end in mind. Database hygiene best practices suggest you to ask yourself the following questions:

  1. What are the goals and expectations for your database?: Having the desired goal will enable your teams to align database priorities to make sure the cleanup procedures favor everyone involved and not just a single business area. The Marketing and Sales departments could primarily benefit from database hygiene practices, but you may also involve other internal stakeholders such as Finance and Accounting.
  2. How do you plan to execute a data cleaning plan?: In line with the question above, you will need to list all the available time, staff, and tools to make this cleanup possible in a seamless way. After all, your business needs to keep running even when data sets go through maintenance processes.

Approaching these questions for the first time can become a challenge on its own, both for you and your team. However, you may be surprised with how database hygiene best practices could help everyone in the long run. 

Related: The Modern Revenue Team: Aligning Marketing Automation & Sales Enablement Systems

5 Best Practices for Data Cleaning

Now that everyone in your company is on the same page, let’s review some database hygiene best practices to keep in your teams’ daily tasks.

1. Develop a Data Quality Plan

Set clear expectations of how the ideal database should be. Create data-quality key performance indicators (KPIs) for every person involved to follow rigorously. What are these KPIs, and how will you and your staff meet them? Which tools will you use to track the health of your data? How will you maintain data hygiene on an ongoing basis?

By consistently applying this best practice, you will learn where the most data quality errors occur, identify incorrect data, and understand the root cause of the data health problems. 

Those inputs will enable you to develop a plan to ensure your data’s health in the future.

2. Standardize Contact Data at the Point of Entry

Faulty data entry procedures are the first cause of “dirty data.” In simple terms, you can’t maintain good data hygiene while also letting unhealthy data enter your CRM.

Before the actual data cleanup happens, check all critical data at the point of entry. This will guarantee standardized information input and will make catching duplicates easier.

This database hygiene best practice suggests you talk to your team about creating a standard operating procedure (SOP) for data entry. Following the SOP will only allow quality data into your CRM at the point of entry.

3. Validate Data Accuracy 

How can you validate data accuracy in real-time? There are helpful tools for data cleaning, such as list imports. You could acquire hygiene tools to include email, phone, and address verification.

Remember: Effective marketing occurs when you use high-quality data and the right set of tools to merge various data sets seamlessly.

4. Identify Duplicates

Duplicate records in your CRM will be a waste of your marketing and sales efforts. Dupes are also expensive in campaign spending and general maintenance. They prevent you from having the essential single customer view. 

Duplicate contacts can also damage your brand’s reputation and guarantee a bad experience for your customer, and they cause inaccurate reporting.

To offer an application example, let’s say you want to perform a HubSpot data cleansing procedure by integrating it with Microsoft Dynamics.

In this scenario, a premium tech tool could provide you with an automated HubSpot to Microsoft Dynamics integration. Such functionality would allow you to merge the power of these marketing automation and CRM platforms to make existing siloed data work in unison. You could save time and money because data entry would be centered in just one place while automatically updating the information on the other platform.

Top solutions like this could let you determine how data should move, transform, and translate, as well as automatically perform data clean-ups.

5. Append Data

At this point, you possibly have some data for each record in your database. For example, you may have their first name, last name, email, and business address for the contact record. Some more complete databases include the person’s title, phone number, annual revenue, their tech stack, and the contact’s location. 

If so many pieces of information are not clean enough, you may inadvertently have General Data Protection Regulation (GDPR) or Canadian Anti-Spam Legislation (CASL) compliance issues. 

Appending data could provide you with the complete information you might skip altogether to avoid breaking these rules.

A Clean Database should Become a Priority

Let’s wrap up this guide by reviewing the benefits of a well-planned data cleaning strategy:

  • Develops and strengthens your customer segmentation
  • Ensures you have a single customer view
  • Avoids any compliance issues with GDPR or CASL
  • Targets customers and prospects in a more effective, personalized way
  • Reduces unnecessary budget spend
  • Increases your overall ROI

Database cleansing strategy success factors:

  1. Ability to detect and remove significant errors and inconsistencies when working with single data sources and when combining multiple sources
  2. Implementation of tools to reduce manual inspection and programming efforts and streamline the process
  3. Deployment in conjunction with schema-related data transformations and specific mapping functions, not by itself

When you can identify the sources of “dirty data” in your database, you can prevent inaccurate or duplicate data from piling up.Vertify, a powerful Revenue Operations solution, won’t let you take your data for granted. Using automated RevOps intelligence features and seamless integrations will help you increase revenue and new customer acquisition with just a few clicks. Request a demo today.

Author: Matt Klepac | CEO | Vertify